Top 50 Python Interview Questions for Data Analysts (2025)

 1. What is Python and why is it popular for data analysis?

2. Differentiate between lists, tuples, and sets in Python.
3. How do you handle missing data in a dataset?
4. What are list comprehensions and how are they useful?
5. Explain Pandas DataFrame and Series.
6. How do you read data from different file formats (CSV, Excel, JSON) in Python?
7. What is the difference between Python’s append() and extend() methods?
8. How do you filter rows in a Pandas DataFrame?
9. Explain the use of groupby() in Pandas with an example.
10. What are lambda functions and how are they used?
11. How do you merge or join two DataFrames?
12. What is the difference between .loc[] and .iloc[] in Pandas?
13. How do you handle duplicates in a DataFrame?
14. Explain how to deal with outliers in data.
15. What is data normalization and how can it be done in Python?
16. Describe different data types in Python.
17. How do you convert data types in Pandas?
18. What are Python dictionaries and how are they useful?
19. How do you write efficient loops in Python?
20. Explain error handling in Python with try-except.
21. How do you perform basic statistical operations in Python?
22. What libraries do you use for data visualization?
23. How do you create plots using Matplotlib or Seaborn?
24. What is the difference between .apply() and .map() in Pandas?
25. How do you export Pandas DataFrames to CSV or Excel files?
26. What is the difference between Python’s range() and xrange()?
27. How can you profile and optimize Python code?
28. What are Python decorators and give a simple example?
29. How do you handle dates and times in Python?
30. Explain list slicing in Python.
31. What are the differences between Python 2 and Python 3?
32. How do you use regular expressions in Python?
33. What is the purpose of the with statement?
34. Explain how to use virtual environments.
35. How do you connect Python with SQL databases?
36. What is the role of the __init__.py file?
37. How do you handle JSON data in Python?
38. What are generator functions and why use them?
39. How do you perform feature engineering with Python?
40. What is the purpose of the Pandas .pivot_table() method?
41. How do you handle categorical data?
42. Explain the difference between deep copy and shallow copy.
43. What is the use of the enumerate() function?
44. How do you detect and handle multicollinearity?
45. How can you improve Python script performance?
46. What are Python’s built-in data structures?
47. How do you automate repetitive data tasks with Python?
48. Explain the use of Assertions in Python.
49. How do you write unit tests in Python?
50. How do you handle large datasets in Python?